SCOPE selects readable hidden layers, constructs conformal gates with IND calibration, and uses supermartingale e-processes to certify persistent service-boundary evidence, improving rejection over final-layer detectors across multiple LLMs and boundary conditions.
Hallucination Detection for Generative Large Language Models by B ayesian Sequential Estimation
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SCOPE: Sequential Conformal Probing for Reliable OOD Rejection in LLM Services
SCOPE selects readable hidden layers, constructs conformal gates with IND calibration, and uses supermartingale e-processes to certify persistent service-boundary evidence, improving rejection over final-layer detectors across multiple LLMs and boundary conditions.